Policy paper

Population Movement Data: unlocking opportunities for a modern digital government

Published 13 August 2025

Executive summary

The Government Digital Service (GDS) is responsible for setting, leading and delivering the vision for a modern digital government. The Blueprint for a Modern Digital Government sets out the plans for making government more joined up, trusted and focused on outcomes that matter to people. The use and reuse of novel data types is central to this ambition. 

Population Movement Data (PMD) shows how people move. It is collected from sources such as mobile phones, apps and card transactions, and is usually anonymised and aggregated for use. These signals are captured using technologies including satellites, cell towers, WiFi, Bluetooth and other location-based methods. It provides timely, location-specific insights that support better planning and service delivery. 

Through 2024-25 GDS led a programme to test how PMD could improve services for citizens, and how to tackle barriers to its wider use.   

Eighteen public sector organisations participated in an Innovation Sandbox, supported by KPMG. This tested how PMD could improve decision-making and explored how the public sector could take a more strategic and effective approach to data procurement. The sandbox gave teams the opportunity to explore real-world use cases, assess the value of different data sources and identify how to overcome barriers to adoption. 

Through this work, participants showed how PMD can generate timely insights that help public services respond more effectively to changing needs. They also demonstrated how it can support more agile, evidence-based decision-making, helping test assumptions, identify gaps and improve how services are targeted and delivered.

Key findings

PMD offers a powerful alternative to traditional data collection methods. Unlike static surveys or manual counts, it provides dynamic, near real-time insights into how people move through and interact with places. This makes it faster, more cost-effective and better suited to capturing patterns of movement over time. For example, PMD can show how different communities use green spaces across seasons, or how visitor flows shift during major events.  These insights can directly inform service planning, infrastructure investment and local economic development. 

The value of PMD lies not only in its speed, but also in its ability to provide flexible, location-based insights that can be adapted to a range of public sector needs. It captures both the ‘where’ and the ‘when’ of population movement, enabling public bodies to respond more precisely to emerging needs. This potential was explored through the Innovation Sandbox, a testing framework in which 18 public sector organisations worked with real PMD sources to trial use cases, challenge assumptions, and identify gaps in existing data. 

Interest in PMD continues to grow, with evidence showing its potential to support digital transformation across a wide range of public services. Looking ahead, technologies such as synthetic data and artificial intelligence may further enhance this potential by simulating movement patterns in data-sparse areas, improving data quality, and unlocking new use cases. 

The PMD market is also evolving. Suppliers now offer a range of products, from web-based dashboards to Application Programming Interfaces (APIs). But challenges remain, including:

• limited transparency about how data products are developed and sourced, and ongoing concerns about bias 

• difficulties validating data, especially where public sector teams have limited in-house expertise 

• complexity in processing large datasets 

• unclear and inconsistent pricing, with high-quality data often perceived as expensive 

• misalignment between existing procurement frameworks and the types of services offered by some PMD providers 

These findings highlight both the promise and the complexity of using PMD in government. Realising its full potential will require coordinated action across the public and commercial sectors to strengthen skills, improve procurement processes and build stronger partnerships.

Translating learnings into practice

To move from trialling the data to operational use, the public sector must create the right conditions for adoption and sustained delivery. The commercial market also has a key role to play by evolving its services to meet the diverse and practical needs of public sector users. 

Through this work, a set of practical steps has been identified to help public sector organisations and the wider market embed the value of PMD more effectively across government. These considerations align with the ambitions of the Blueprint for Modern Digital Government and can contribute to accelerating its delivery. By improving how the public sector accesses, uses, and procures data such as PMD, it becomes possible to strengthen collaboration, make smarter investments, and unlock new opportunities to deliver more targeted, trusted, and outcome-focused services.

Public sector

• Allow time for innovation: give teams the space to regularly experiment with population movement data beyond their initial use cases. Testing and adapting the data in different contexts often reveals new value. 

• Assess, match and grow capabilities: choose tools such as dashboards or application programming interfaces that match teams’ current skills, and use them to build confidence and capability over time. 

• Design procurement around collaboration: design procurement processes that encourage ongoing collaboration between suppliers and users. This helps ensure data products evolve to meet real needs and remain usable over time. 

• Promote transparency in data use: share how data is baselined, modelled and assessed for quality. This helps others understand how to use population movement data effectively and manage potential bias. 

• Encourage cross-sector learning: share use cases and lessons learned to build a shared understanding of where population movement data works well, and where it does not. 

• Ensure appropriate use: population movement data can reflect inconsistent patterns depending on how it is collected, modelled or combined with other datasets. Public sector teams should continue to critically assess the representativeness of the data and consider how any biases might affect decisions or outcomes.

Commercial market

• Design for diverse public sector needs: offer a range of service models, from simple dashboards to direct data access, to meet the different needs and capabilities of public sector users. 

• Enable user feedback: build in ways for users to share feedback, challenges and ideas. This helps improve how relevant and usable products are in practice. 

• Support capability building: provide clear guidance, training and examples to help public sector teams understand and use population movement data effectively. 

• Be transparent about methods: clearly explain how data is collected, processed and modelled, including any limitations or known biases.

• Collaborate on standards: work with others to develop shared standards for data quality, interoperability and appropriate use. This builds trust and makes it easier for the public sector to use population movement data confidently.

Introduction

PMD has long been used to understand how people travel, commute and interact with places. This data was traditionally gathered through surveys, manual counts and censuses. Today this information can be captured at scale and in near real time using everyday digital devices such as smartphones, wearables and payment cards. 

During the COVID-19 pandemic, government used these new forms of PMD to understand how people were moving around the country and how this influenced the spread of the virus.  These insights supported more targeted public health interventions, informed decisions on lockdowns and transport, and helped allocate resources more effectively at both national and local levels. 

Since then, interest in PMD has grown across the public sector, with organisations exploring its use in areas such as transport planning, emergency response and local development. The private sector has also adopted PMD to improve decision-making. It has been shown to generate commercial and operational value by providing a clearer, timelier picture of how people interact with places. Real estate professionals now use movement insights to inform site identification, property valuation and investment strategies. Marketers use it to understand where and when target audiences are most active, helping them tailor campaigns and improve return on investment.   

In 2023 GDS set up a programme of work to explore public sector use of this type of PMD in more depth and realise a wider range of benefits. As part of this work, GDS spoke with data providers, researchers and public sector users to understand the challenges they face in using and providing this data, as well as their views on its future potential. In partnership with KPMG, the programme hosted an Innovation Sandbox – a — testing framework where data from O2 Motion, BT Active Intelligence, Huq, Strava Metro and VISA was used in real-world scenarios by 18 different public sector organisations. Alongside this, KPMG also undertook exploratory research, and some of the insights generated reflect both their work and that of the GDS team. 

This report presents key findings and outlines practical steps that public sector organisations and the broader market can take to maximise the value of PMD

Key findings

The sandbox provided an opportunity to examine how the PMD market operates in practice and how it aligns with public sector needs. Engagement with suppliers and users revealed that, although the market has expanded to offer a broader range of data products and access models, it remains complex and challenging to navigate. Persistent issues around transparency, pricing, and data quality continue to limit effective use by public sector teams. At the same time, the programme highlighted the importance of collaboration between public sector users and commercial providers in unlocking value. These findings informed a deeper understanding of the strategic and practical benefits of PMD, the structure and dynamics of the market, evolving patterns of data demand and quality, limitations, and emerging trends.

Strategic and practical benefits

PMD offers both strategic and practical benefits for public sector organisations, particularly in areas such as transport planning, emergency response, and infrastructure development. When integrated thoughtfully into planning and operations, PMD can support more informed, data-driven decisions.

Strategic benefits

One of the key strategic advantages of PMD is its ability to complement traditional data sources, offering a second layer of insight that can strengthen decision-making. Participants in the Innovation Sandbox found that PMD helped validate or challenge assumptions drawn from other datasets, supporting more robust and confident decisions. 

For example, the Ministry of Housing, Communities and Local Government (MHCLG) used the sandbox to trial PMD in a controlled setting. By combining PMD with existing data, they were able to identify areas where additional support was most needed, helping to inform strategic decisions about resource allocation.

Innovation Sandbox Case Study: Ministry of Housing, Communities and Local Government (MHCLG) – Informing Strategic Planning with PMD

As part of the Innovation Sandbox, MHCLG explored how PMD could enhance its understanding of functional economic areas in England. This work supported the Government’s strategic objective, outlined in the English Devolution White Paper, to devolve powers to new Strategic Authorities responsible for local decision-making.

MHCLG used PMD to analyse travel patterns and identify clusters of concentrated movement, offering a more dynamic and current view of economic geography than traditional census-based travel-to-work area analysis. The data provided insights into journey purposes and allowed segmentation by time of day and week, helping to reveal how people interact with places in real time.

This strategic use of PMD enabled MHCLG to identify potential priority areas for new strategic authorities and informed internal discussions on devolution planning. Evidence derived from PMD was also included in public consultations for the Devolution Priority Programme, demonstrating how the data supported more informed, place-based policy decisions.

Practical benefits

PMD also offers a range of practical advantages over traditional data sources. Custom surveys and manual data collection can be costly, inflexible, and quickly outdated. In contrast, PMD can be: 

• Efficient – easier and faster to collect, 

• Flexible – adaptable to different needs, 

• Timely – often available in near real-time. 

The sandbox gave several organisations a safe space to test these benefits in real-world scenarios. For instance, Defra explored how PMD could be used to understand visitor flows in natural spaces, helping to inform operational planning and resource management.  

PMD is collected continuously through everyday services and typically does not require additional on-the-ground infrastructure like cameras or stationary sensors that must be physically installed in specific locations. It also allows access to historical data, enabling analysis of trends and the impact of events over time.  

Accessing PMD through APIs was also highlighted as a practical advantage. APIs simplify data retrieval, reduce the burden on local computing and storage, and improve the ability to visualise and analyse data efficiently. 

Finally, PMD offers high spatiotemporal granularity, showing where and when people are moving across wide geographic areas and time periods. This level of detail is difficult to achieve with other methods and can provide a fresh insight on location data. It can be used to enhance, supplement, or even replace existing data sources, as demonstrated in the Department for Environment, Food and Rural Affairs case study below.

Innovation sandbox case study: Department for the Environment, Food and Rural Affairs (Defra) – Enhancing local monitoring of natural space use

Defra, alongside Natural England and the Office for National Statistics (ONS), explored the use of PMD to address a key limitation in their existing data collection methods. Their current approach, the People and Nature Survey (PaNS), provides valuable insights into the demographics and behaviours of visitors to natural spaces. However, due to sample size constraints, PaNS can only produce reliable estimates at the national level, limiting its usefulness for local planning and management.

To overcome this, the team ran a proof of concept using app-based PMD—specifically Strava Metro—combined with ground truth data and other contextual sources. The goal was to test whether PMD could be used to model visit patterns at a more local scale. The results showed that it is indeed possible to generate reliable estimates of visits to natural spaces using this approach.

Building on this success, Defra and Natural England are now partnering with the University of Exeter to extend the modelling work. The next phase will incorporate machine learning, additional ground truth data, and a broader range of app-based PMD sources. The aim is to develop open, scalable tools for monitoring recreational activity at sub-regional levels.

This approach offers a practical and cost-effective alternative to traditional survey methods. It enables more timely, flexible, and granular insights into how people use natural spaces, such as national parks, nature reserves and trails, and can support decisions about visitor management, such as where to add new paths, relocate car parks, or create new green spaces.

Market structure and dynamics

The PMD market is broad and complex, made up of many different types of organisations. These include: 

• Mobile Network Operators (MNOs), such as Vodafone, BT and O2 

• Credit and debit card companies, including Visa and Mastercard 

• Mobile apps, such as fitness platforms like Strava or social media platforms like Meta 

• Aggregators of data from mobile apps such as those collecting location data from weather apps or utility apps.  

• Data resellers, who combine and repackage data from multiple sources 

Some organisations that hold PMD do not routinely make it available to the public sector. This may reflect internal considerations such as data governance policies, commercial strategies or assessments of public or monetary benefit. For example, Google made its PMD available during the COVID-19 pandemic to support public health responses, but discontinued access in 2022 as part of a broader shift in its data-sharing approach.  

The market has grown in both the quality and variety of data available, with access methods ranging from APIs to interactive dashboards. These tools cater to a wide range of technical abilities and use cases. However, transparency varies. MNOs are generally open about data sources, though their sharing practices differ. Vodafone, for instance, offers customers an explicit opt-out scheme, while others rely on terms and conditions. App aggregators and data resellers often operate under complex commercial confidentiality agreements, which can limit how much they disclose about data provenance or methodology.  

Final data products are typically modelled outputs, combining raw signals, such as mobile device pings or card transactions, with algorithms to estimate broader trends. This modelling adds value but also introduces complexity, sometimes obscuring the original data sources. Pricing structures are also inconsistent and often opaque, varying by use case and demand. Many high-value datasets are commercially licensed and priced beyond the reach of some public sector organisations. 

Some datasets are available free of charge for specific purposes. For example, Meta’s Data for Good  is primarily used to support research, while Strava Metro provides access to mobility data for a range of public interest projects. The World Pop applied research group at the University of Southampton has used Meta data for crisis responses and social good. Alongside this, WorldPop has a diverse portfolio of projects that utilises PMD from across the world. A practical application of this can be seen in WorldPop’s use of mobile network data to guide malaria control efforts in Namibia.

Using PMD to support malaria elimination efforts in Namibia

WorldPop is an applied research group at the University of Southampton, led by Professor Andrew Tatem, that enhances small-area population estimates in low- and middle-income countries by integrating open-source and mobile data. This approach is particularly valuable in regions where census data is outdated or incomplete.

In Namibia, WorldPop collaborated with the National Vector Borne Disease Control Programme to support malaria elimination efforts. The team used call detail records (CDRs), provided by MTC Namibia, to map population movement across the country. These mobility patterns were then combined with malaria case data to identify areas where malaria was being sustained and areas where it was spreading.

This analysis enabled the Namibian government to target interventions more effectively. Resources were concentrated in areas with ongoing transmission, while lower-risk areas were monitored using lighter-touch approaches. The result was a more efficient and evidence-based strategy for planning malaria control activities.

Around the world, countries and regions are increasingly exploring the potential of PMD as part of their evolving data strategies. The European Union has introduced a comprehensive data strategy aimed at improving accessibility, interoperability, and standardisation across sectors, which provides a foundation for exploring PMD. Within this framework, some member states are independently assessing the value of PMD. For example, Germany’s Federal Statistical Office has conducted feasibility studies using mobile network data to enhance the accuracy of commuter statistics.These studies have informed updates to transport modelling and planning, particularly in areas where traditional survey data is limited.  

In the United States, the Census Bureau uses anonymised mobile device data to better understand population movement and migration patterns. This has enabled more timely and detailed insights into short- and long-distance moves across counties and states, supporting emergency response planning, resource allocation and improvements to official migration statistics.  

In Japan, the government has adopted a system called Spectee Pro, which uses artificial intelligence to analyse real-time data from mobile networks, traffic sensors and social media. More than 1,100 local governments have used the system to issue faster evacuation orders and allocate emergency services more precisely during natural disasters such as floods and typhoons.  

By examining these approaches, the UK can identify opportunities to adapt and apply similar strategies to strengthen its own data capabilities and policy outcomes.

UN global working group on big data – Transport and commuting subgroup The United Nations, through its Department of Economic and Social Affairs – Statistics Division, has recognised the growing importance of mobile phone network data in producing official statistics. In response, it has established the Committee of Experts on Big Data and Data Science for Official Statistics (UN-CEBD), including a dedicated Task Team on Mobile Phone Data (TT-MPD).

This task team works with national statistical offices to develop standards, methodologies and capacity for using mobile phone data to inform transport-related Sustainable Development Goals. These include reducing road traffic fatalities, improving access to sustainable transport, and developing resilient infrastructure.

One key impact of this work has been the development of technical guidelines and pilot projects that have enabled countries to replace or supplement traditional commuting surveys with mobile phone data. This has the potential to led to more timely and granular insights into commuting patterns, which in turn can support transport planning and infrastructure investment decisions in participating countries. The initiative demonstrates how mobile phone data can be responsibly integrated into official statistics to improve decision-making and service delivery at national and international levels.

Data demand and quality

PMD is increasingly valued across the public sector for its ability to deliver large sample sizes and near real-time insights. These features make it a powerful tool for understanding population movement and behaviour at scale. However, while PMD offers broader coverage than traditional sources, some users find it more difficult to assess data quality or account for potential bias in derived outputs. 

Experienced users noted that smaller, well-established datasets are often easier to evaluate, largely because their limitations are better understood. In contrast, the complexity and limited transparency of some large PMD datasets can raise concerns, even when the data itself is more comprehensive. 

To address these challenges, public sector teams often take a cautious approach to evaluating PMD. Many review sample outputs before committing to a purchase or compare results against known benchmarks. For example, the Department for Transport have previously used the National Travel Survey to benchmark movement data sets.

Combining PMD with other datasets, whether during supplier processing or user analysis, can significantly enhance its value and provide deeper insights for targeted decision-making. However, integration also brings challenges. Variations in how datasets are collected, modelled, or structured can lead to inconsistencies that affect the reliability of the results. These differences can also reduce transparency, making it more difficult to understand how conclusions were reached. 

To manage these risks, experienced users recommend maintaining close collaboration with data providers throughout the contract period. This helps clarify assumptions, resolve technical issues, and ensure the data remains useful and appropriately applied. 

Cost remains a significant barrier to wider adoption. While some PMD is freely available, many of the most detailed and valuable datasets are commercially licensed, which can limit access for public sector organisations with constrained budgets. 

The market is beginning to respond with more flexible options, such as basic trend data, which can still deliver value at a lower cost. In addition, some organisations are exploring creative approaches to cost-sharing. For example, the Greater London Authority’s High Streets Data Service pools data centrally, making access more affordable and practical for London boroughs and other partners.

Collaborative data access for high streets

The Greater London Authority (GLA)’s High Street Data Service (HSDS) is a collaborative data-sharing partnership between the GLA, London boroughs, and business improvement districts (BIDs) established in 2021 to support high streets in their pandemic recovery. HSDS provides its members with access to high-quality data, analytical tools, and reports on spending, footfall and vacancy, allowing them to tailor interventions and strategies to support high street businesses and improve the overall visitor experience.

The HSDS partnership involves collaboration with data providers like Mastercard and BT Active Intelligence, which supply population movement data. The GLA combines this data with other sources, such as pedestrian counter data, to create a comprehensive view of high street activity. Currently, 18 London boroughs and 17 BIDs subscribe to the service, each saving up to £20,000 annually on data costs through the collective purchasing model.

The use of mobile phone data allows for a better understanding of how people move around and interact with high street spaces.

An additional barrier is that procurement frameworks are often poorly suited to PMD suppliers, who don’t fit neatly into standard categories like software or consultancy. This makes procurement inefficient and time-consuming. It creates barriers for public sector teams trying to source appropriate services, and for suppliers who struggle to present their offerings within existing frameworks. As a result of this finding, steps have already been taken to include PMD services as a standalone sub-filter in the Space Technology Solutions Dynamic Market.

Limitations

PMD has demonstrated broad usefulness across a variety of policy areas and operational settings, at both national and local levels. Participating organisations identified a wide range of applications. However, some limitations were also observed. PMD was less effective in situations that require a high degree of statistical precision, such as detailed sampling. For example, the Office for National Statistics explored whether PMD could be used to replicate Travel to Work Areas, which are usually based on commuting data from the Census. The data available in the sandbox did not include enough detail, such as the purpose of trips, to support this type of analysis effectively.

Innovation sandbox case study: Plymouth city council

Plymouth City Council (PCC) used PMD through the Innovation Sandbox to gain insights into travel patterns within Plymouth and its surrounding areas. The goal was to better understand not only the catchment areas of local urban centres, but also the broader strategic reach of Plymouth across Devon and Cornwall. As a unitary authority responsible for strategic planning and transport, PCC used PMD to analyse where people travel to and from for various local centres. This analysis provided valuable information on regular journey patterns, including travel volumes, key destinations, and the differing catchments of local centres. This data-driven approach offered cost-effective insights that may not have been identified through traditional methods.

PMD provided capabilities that traditional data sources, such as census data or bespoke surveys, could not match. While census data offers a static snapshot and bespoke surveys are often costly, PMD delivers up-to-date, comprehensive travel data that can be segmented by time of day and day of the week. This innovative approach confirmed that Plymouth’s main catchment extends into Cornwall, supporting more effective local planning and infrastructure investment.

Access to PMD has also sparked new conversations about the role of local centres and the travel behaviours of residents. It has provided a clearer picture of Plymouth’s strategic catchment in relation to neighbouring areas, offering a level of insight that was previously unavailable.

The sandbox also highlighted that understanding where PMD is less effective in deriving insights is as valuable as identifying where it performs well. Even in cases where PMD was not a perfect fit, its exploration prompted new lines of inquiry. The British Geological Survey (BGS), for example, used the sandbox to investigate how PMD might complement their existing data and methods.

Innovation sandbox case study: British Geological Survey (BGS) - local contingency planning 

BGS explored the use of PMD through the Innovation Sandbox to assess its potential for enhancing exposure and vulnerability assessments for landslides in Scotland, particularly in rural and tourist-heavy areas where conventional traffic data is limited. The initial focus was a scoping evaluation exploring how dynamic movement data could support the development of exposure models to inform contingency planning, early warning, and disaster response.

Using Huq data, BGS conducted exploratory analysis of visitor footfall, dwell time, and origin-destination patterns to identify high-risk locations such as walking trails and isolated settlements. This helped test the potential to quantify exposure duration and assess the vulnerability of both residents and transient populations. Demographic data and visit frequency were also reviewed for their utility in understanding socio-economic impacts and seasonal variation.

While assessment of the data did not progress beyond the exploratory phase, the analysis highlighted the added value of PMD over static census and traffic data. These early insights demonstrate the potential to improve preparedness strategies, inform national risk assessments, and support evidence-based approaches to disaster risk reduction and community resilience, an important strategic research area in BGS.

Testing also surfaced several technical challenges associated with working with PMD. The datasets involved were often extremely large, sometimes containing billions of rows, which created difficulties during both download and analysis. In some cases, teams had to pause their work due to long download times or because the size of the files made analysis too slow to complete. Its technical demands can also present barriers to use, particularly for teams without access to high-performance computing environments or streamlined data handling workflows. 

Additionally, some teams found that simpler, web-based tools offered faster access, though they often lacked the full detail of the raw datasets. Others encountered limitations due to insufficient hardware or system capacity to process large files efficiently. These challenges highlighted the importance of having appropriate tools and infrastructure in place.

Emerging trends

The PMD market is evolving, with a range of opportunities emerging for innovation and policy development. To reduce the costs and biases associated with collecting PMD, there is growing interest in the use of synthetic data, which is artificially generated to reflect patterns observed in the real world. For example, companies such as Unacast in Norway are already using generative artificial intelligence to create new data samples that replicate existing foot traffic patterns. These synthetic datasets are being used to forecast future trends under different scenarios and to simulate operational strategies. 

Emerging technologies like AI are set to impact the PMD market significantly. AI, particularly the application of Large Language Models, can make data more accessible by letting people ask questions and get answers without needed technical skills or analytical experience. There is growing potential for international collaboration in the field of PMD and AI, particularly in areas such as autonomous vehicles, AI-powered transport systems, and smart infrastructure. These global efforts can help accelerate innovation and ensure that emerging technologies are developed with shared standards and interoperability in mind. 

At the same time, the UK has a valuable opportunity to learn from international best practices and strengthen partnerships with the private sector. An example of this is the partnership between the Italian National Institute of Statistics (ISTAT) and Vodafone Business Italia below. By doing so, it can better harness the benefits of PMD and related technologies to support more efficient, future-ready public services.

ISTAT and Vodafone: A collaborative approach to exploring PMD

In 2019, the Italian National Institute of Statistics (ISTAT) partnered with Vodafone Business Italia in a cost-free collaboration to explore the potential of mobile network data for enhancing official statistics. Through this partnership, ISTAT gained access to Vodafone’s analytics, enabling comparisons with official statistical standards. In return, Vodafone was able to benchmark its data against national metrics.

The collaboration focused on sectors such as tourism, mobility, and smart cities. It provided valuable insights into tourist flows, demonstrating how mobile data could complement traditional surveys by offering a more detailed and timely view of tourist movements. However, the project also highlighted challenges, including the need to align mobile data with official tourism definitions and to address privacy concerns.

From learnings to action

Unlocking the full value of PMD will require coordinated action across both the public sector and the commercial market. While the Innovation Sandbox demonstrated the potential of PMD to support more responsive, data-informed public services, it also highlighted the structural, technical, and cultural changes needed to embed its use more widely and sustainably. 

The public sector must create the right conditions for PMD to thrive—by building internal capabilities, fostering ongoing collaboration with data providers, and embedding transparent, appropriate data practices. At the same time, the commercial market must continue evolving its services to meet the diverse, practical needs of public service delivery.

The public sector  

Public sector organisations can take practical steps to maximise the value of PMD for their organisation.  

1. Allow time for innovation 

Teams need regular time and space to explore PMD beyond their initial use cases. In the sandbox, participants who had the flexibility to test the data in different contexts were more likely to uncover new insights and applications. However, many faced time constraints that limited experimentation. Building time for innovation into project planning and delivery cycles will help teams realise the full potential of PMD. 

2. Assess, match and grow capabilities 

PMD can be used by teams with a range of technical skills, but the format in which it is delivered matters. Dashboards and map interfaces are more accessible to non-specialists, while APIs and raw data require more advanced capabilities. Matching tools to current skills and using them as a foundation to build confidence and capabilities can help teams make better use of the data over time. This also supports longer term goals to build digital and data capabilities in the public sector. 

3. Design procurement around collaboration: 

Flexible contracts are particularly valuable when working with PMD. Evidence from the sandbox and follow-up interviews showed that the most effective outcomes occurred when suppliers and users engaged in ongoing, collaborative dialogue. Procurement processes should be designed to support this kind of partnership by embedding flexibility, iterative feedback, and shared problem-solving into contracts. This helps ensure that data remains relevant, usable, and responsive to evolving public sector needs. 

4. Promote transparency in data use 

Sharing how data is baselined, modelled, and assessed for quality helps build trust and understanding across the public sector. For example, if a dataset such as ticketing data is useful for validating specific PMD in transport analysis, sharing that data or the insights gained from it can help others understand how PMD has been baselined and evaluated. This supports better decision-making by making it easier to identify and address potential sources of bias. Transparency in methods and assumptions is especially important when PMD is used to inform policy or allocate resources. 

5. Encourage cross-sector learning 

Knowledge about PMD is often fragmented, and valuable insights can be lost due to staff turnover or siloed working. The sandbox highlighted the value of peer learning, through communities of practice, shared code, and informal collaboration. Public sector organisations should actively share use cases, lessons learned, and practical examples to build a collective understanding of where PMD works well, and where it may not. 

6. Appropriate use 

While PMD is typically anonymised and aggregated, our work in the Innovation Sandbox showed that appropriate use depends on more than just technical safeguards. Participants noted that PMD can reflect inconsistent patterns depending on how it is collected, modelled, or combined with other datasets. This raised concerns about representativeness and potential bias. These findings highlight the importance of critically assessing how representative the data is and considering how any limitations or biases might influence decisions or outcomes. 

The wider commercial market 

Suppliers play a critical role in enabling the wider use of PMD. The sandbox showed that public sector teams benefit most when commercial offerings are flexible, transparent, and supported by clear guidance. The following actions can help suppliers better meet the needs of public sector users. 

1. Design for diverse public sector needs 

Public sector organisations vary widely in their skills, capabilities and capacities. A single product or delivery model will not suit all users. Suppliers should offer a range of service models—from simple dashboards to direct data access—so that organisations can choose the format that best fits their needs. This flexibility will help lower the barrier to entry and encourage broader adoption. 

2. Enable user feedback 

The most effective PMD products are those that evolve in response to user needs. Suppliers should build in mechanisms for public sector users to share feedback, raise challenges, and suggest improvements. This could include user forums, regular check-ins, or embedded support channels. Continuous engagement helps ensure that products remain relevant, usable, and aligned with real-world requirements. 

3. Support capability-building 

Many public sector teams need more support to use PMD effectively. Suppliers can help by providing clear, user-friendly documentation, training materials, and worked examples. In the sandbox, a lack of guidance and metadata often slowed down analysis and created confusion. Addressing this gap would make it easier for teams to get started, build confidence, and use the data more effectively. 

4. Be transparent about methods 

Suppliers should clearly explain how their data is collected, processed, and modelled—including any known limitations or biases. This helps users assess whether the data is suitable for their needs and apply it appropriately. Transparency also builds trust and supports more informed decision-making. 

5. Collaborate on standards 

As the PMD market matures, there is a growing need for shared standards around data quality, interoperability, and ethical use. Suppliers should actively contribute to these efforts, working with government, academia, and civil society to develop common frameworks that support responsible innovation. This will help ensure that PMD can be used confidently and consistently across the public sector.

Conclusion

PMD can offer real value to the public sector, enabling faster, more flexible, and more informed decision-making across a range of policy areas. From transport planning to local economic development, PMD provides a richer, more dynamic understanding of how people move and interact with places. It complements traditional data sources and, in many cases, offers insights that would otherwise be difficult or costly to obtain. 

However, unlocking this value is not automatic. It requires deliberate action by both the public sector and the wider market to address barriers around capability, procurement, transparency, and appropriate use. The considerations set out in this report are designed to help organisations move from experimentation to operational use, embedding PMD more effectively into everyday decision making. 

By improving how data like PMD is accessed, shared, and applied, government can make smarter investments, strengthen collaboration, and deliver more targeted services that meet the evolving needs of people and communities. 

PMD is not a silver bullet, but when used well it can be a powerful tool for public value. The opportunity now is to build on the momentum of this programme and take the practical steps needed to make PMD a routine part of how government understands and serves the public.

Annex

Innovation sandbox participants

• British Geological Survey 

• Cabinet Office 

• Defence Science and Technology Laboratory 

• Department for the Environment, Food and Rural Affairs  

• Department for Transport 

• Greater Manchester Combined Authority  

• Home Office 

• Met Office 

• Ministry of Housing, Communities and Local Government 

• Office for National Statistics 

• Plymouth City Council 

• Sport England 

• Stockport Council 

• The UK Health Security Agency 

• Trafford Council  

• Valuation Office Agency 

• Visit Britain 

• West London Alliance

Market study participants

• Amey 

• BT Active Intelligence 

• City, University of London  

• Meta, Data for Good 

• Experian  

• Geographic Data Service 

• Google  

• Healthy and Sustainable Places Data Service 

• Huq Industries 

• Mastercard  

• Revolut  

• Strava Metro  

• Tech UK 

• Vodafone 

• Visa  

• WorldPop,University of Southampton

Wider public sector interviewees

Individuals with prior experience using PMD outside of the Innovation Sandbox were interviewed from the following public sector organisations: 

• Department for Culture, Media and Sport 

• Greater London Authority 

• National Highways 

• Cabinet Office 

• Network Rail 

• The Office for National Statistics 

• Transport for London 

• Transport Scotland 

• Transport for Wales